a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing

This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.

Methods
The dataset was collected via numerical experiments performed primarily with the code AERO-F (https://bitbucket.org/frg/aero-f), which was also used for processing the data (e.g. reporting interated forces and moments, etc.), with the addition of AERO-S (https://bitbucket.org/frg/aero-s), rompc (https://github.com/StanfordASL/rompc), asl_fixedwing (https://github.com/StanfordASL/asl_fixedwing), and the asl-v3.2.0 branch of qpOASES (https://github.com/jlorenze/qpOASES; forked from https://github.com/coin-or/qpOASES), which were used for the simulation of the controller. Data was also processed, and some numerical experiments performed, with code found at pyaeroutils (https://github.com/amcclell/pyaeroutils), and using CVX (http://cvxr.com/cvx). Additional processing scripts are included with the dataset, and the top-level directory contains a README.txt describing additional dependencies.

本研究在若干假设条件下，提出了一种面向飞行器自主着陆的两级数据驱动数字孪生（digital twin）框架。该框架包含一个面向模型预测控制（model predictive control）的数字孪生实例，以及一个面向流固耦合（fluid-structure interaction）与飞行动力学（flight dynamics）的创新型实时数字孪生原型，为前者提供信息支撑。后者基于飞行动力学高保真粘性非线性计算模型，沿预先设计的下滑轨迹进行线性化，并将其投影至低维近似子空间，以在保证精度的同时实现实时性。其核心功能为在飞行过程中实时预测飞行器的状态以及作用于其上的气动力与气动力矩。与基于稳态风洞数据的静态查找表或基于回归的代理模型（surrogate models）不同，上述实时数字孪生原型可使模型预测控制的数字孪生实例采用真正的动态飞行模型进行信息更新，而非精度有限的稳态气动力与气动力矩数据集。本文详细阐述了所提出的两级数字孪生框架的构建方法，并通过数值仿真对其进行了验证；同时报告了针对斯坦福大学加装设备的现成商用无人机（unmanned aerial vehicle），在自主模式下开展的初步飞行验证结果。

方法
本数据集主要通过数值实验采集，实验核心代码为AERO-F（https://bitbucket.org/frg/aero-f），该代码同时用于数据处理（例如输出集成气动力与气动力矩等结果）；此外还使用了AERO-S（https://bitbucket.org/frg/aero-s）、rompc（https://github.com/StanfordASL/rompc）、asl_fixedwing（https://github.com/StanfordASL/asl_fixedwing）以及qpOASES的asl-v3.2.0分支（https://github.com/jlorenze/qpOASES；源自https://github.com/coin-or/qpOASES），上述工具均用于控制器仿真。此外，本数据集还通过pyaeroutils（https://github.com/amcclell/pyaeroutils）中的代码以及CVX（http://cvxr.com/cvx）工具进行了数据处理与部分数值实验。数据集附带额外的处理脚本，顶层目录包含README.txt文件，用于说明额外的依赖项。